Non-stationary physiological audio signals like phonocardiogram (PCG) often contain sufficient noisy components that cause further decision making and analyses highly error-prone. Detection or identification of noisy non-stationary physiological audio signals through automated methods would imply that further analysis is done only on clean non-stationary physiological audio signal. For instance, automated classification of pathology in heart sound recordings has been performed for over 50 years, but still presents challenges. Current studies for heart sound classification are flawed because they predominantly validate only clean recordings. However, in practice PCG recordings have poor signal quality and often there exists high amount of noise. It is thus imperative to further extract a lightly noisy component of the recordings from the otherwise rejected noisy component to ensure that critical information in the lightly noisy components are not missed out during further analyses.